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Record W1548590899 · doi:10.5539/ies.v8n6p142

The Development of Educational Technology Policies (1996-2012) Lessons from China and the USA

2015· article· en· W1548590899 on OpenAlexvenueno aff
Alnuaman Alamin, Guo Shao-qing, Zhang Le

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsChinaEducational technologyInformation and Communications TechnologyEconomic growthLiteracyPolitical scienceTechnology integrationTechnology educationSociologyPublic relationsPedagogyEconomics

Abstract

fetched live from OpenAlex

This study reviews the development of educational technology macro policies in China and USA based on the historical juxtaposition approach. It shows that, despite the fact that two countries have major differences, with China officially being a socialist country, while the USA is a capitalist country; the development of educational technology policies in the two countries has displayed remarkable similarities. USA’s revised educational technology plans also were used as the basis of some of Chinese educational technology policies. The study displays how the ambitions toward integrating ICT in education moved from critical issues facing elementary and secondary schools in the USA like technology literacy after 1990, to adopting a technology-based learning model that resulted in measuring of student’s outcomes individually in NETP 2010. China has moved from using traditional technology in education in the early 1990 e.g. slides, projectors, films, and radio and aims at building an educational infrastructure close to the developed countries by 2020. The study reviews briefly the development of equipping American and Chinese schools with the ICT hardware infrastructure and the educational technology standards in both countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.095
GPT teacher head0.451
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2015
Admission routes1
Has abstractyes

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